We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching, where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers’ knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work, two methods are investigated to augment the limited training set in practical operations, namely, oscillating the feature weight and revising the case feature in the case feature vectors. In addition, we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing, and the results demonstrate the benefit of the augmented training set. Moreover, experimental results reveal that our approach outperforms other matching approaches.

我们提出一项探索性研究，以提高产品设计中知识推送系统的性能。我们专注于知识匹配的领域，在该领域中，传统的匹配算法需要重复计算，从而导致响应时间长，并且需要提高准确性。我们方法的目标是通过知识推送系统中的快速响应和优质服务来满足设计师的知识需求。为了改进先前的工作，研究了两种方法来增加实际操作中的有限训练集，即，振荡特征权重和修改案例特征向量中的案例特征。此外，我们提出了一种多分类径向基函数神经网络，该神经网络可以一次匹配知识库中的知识并确保推送结果的准确性。我们使用训练集将我们的方法应用于计算机数控机床的指南设计中，以进行训练和测试，结果证明了增强训练集的好处。此外，实验结果表明，我们的方法优于其他匹配方法。